Pest detector to identify a type of pest using machine learning

ABSTRACT

In some implementations, a pest detector may receive sensor data from a sensor. The pest detector may determine, using a machine learning algorithm, that the sensor data indicates a presence of a first type of pest. The pest detector may send a notification message, including at least a portion of the sensor data, to a computing device and visually indicate that the first type of pest was detected using an indicator. The pest detector may receive an update to the machine learning algorithm from a server and install the update to create an updated machine learning algorithm. The pest detector may receive second sensor data and determine, using the updated machine learning algorithm, that the second sensor data indicates the presence of a second type of pest that is not recognized by the machine learning algorithm.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present patent application is a continuation of U.S. patentapplication Ser. No. 16/697,893 entitled “PEST DETECTOR TO IDENTIFY ATYPE OF PEST USING MACHINE LEARNING” filed on Nov. 27, 2019, which is acontinuation of U.S. Pat. No. 10,524,461 filed on Sep. 25, 2018 entitled“PEST DETECTOR TO IDENTIFY A TYPE OF PEST USING MACHINE LEARNING,” bothof which are incorporated by reference herein in their entirety and forall purposes as if completely and fully set forth herein.

BACKGROUND OF THE INVENTION Field of the Invention

The invention relates to a software application to monitor a status ofeach of one or more pest detectors deployed in a particular location(e.g., a building).

Description of the Related Art

Pest detection and pest control have remained largely unchanged for manyyears. Modern pest control is decidedly low-tech, with glue traps ormechanical traps being manually baited with peanut butter or similarattractants. Once traps are set, a user (e.g., either a homeowner or apest control service) must manually examine each trap to avoid having atrapped pest slowly die and decay over time and to re-bait each trap asneeded.

Pest detection is similarly low tech and requires that a homeownereither view the pest directly or view an effect of the pest. Most pestsin a house tend to be nocturnal so homeowners rarely view pestsdirectly. For example, an occupant of a home may observe a cockroachwhen they get up in the middle of the night and turn the lights on inthe kitchen to get something to eat and/or drink. As another example, anoccupant may view the effect of the pest, e.g., rat droppings, gnawmarks, and the like. Because of this, many types of pests may goundetected for long periods of time.

SUMMARY OF THE INVENTION

In some implementations, a pest detector may receive sensor data from asensor. The pest detector may determine, using a machine learningalgorithm, that the sensor data indicates a presence of a first type ofpest. The pest detector may send a notification message, including atleast a portion of the sensor data, to a computing device and visuallyindicate that the first type of pest was detected using an indicator.The pest detector may receive an update to the machine learningalgorithm from a server and install the update to create an updatedmachine learning algorithm. The pest detector may receive second sensordata and determine, using the updated machine learning algorithm, thatthe second sensor data indicates the presence of a second type of pestthat is not recognized by the machine learning algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a detector, according to someembodiments.

FIG. 2 is a block diagram illustrating connecting a detector capable ofaccepting modular plugins, according to some embodiments.

FIG. 3 is a block diagram illustrating detection zones of a detectoraccording to some embodiments.

FIG. 4 is a block diagram illustrating a detector to detect movementwithin a wall, according to some embodiments.

FIG. 5 is a block diagram illustrating a detector that includes a swivelplug, according to some embodiments.

FIG. 6 is a block diagram of a system that includes multiple detectorsconnected to a network, according to some embodiments.

FIG. 7 is a block diagram illustrating a user interface to displaylocations of detectors in a floor plan, according to some embodiments.

FIG. 8 is a block diagram illustrating a user interface to display datagathered by a detector, according to some embodiments.

FIG. 9 is a flowchart of a process that includes identifying a type ofpest using sensor data, according to some embodiments.

FIG. 10 is a flowchart of a process that includes displaying one or morepredictions regarding pests, according to some embodiments.

FIG. 11 illustrates an example configuration of a computing device thatcan be used to implement the systems and techniques described herein.

DETAILED DESCRIPTION

The systems and techniques herein describe a pest detector that candetect and identify pests and send an alert to a computing deviceassociated with a homeowner or a pest control service. The pest detectormay include one or more sensors, such as, for example, a motion sensor,an imaging sensor (e.g., a camera), an audio transducer (e.g.,microphone), a structured light sensor, an ultrasound sensor, aninfrared imaging sensor, a temperature (e.g., a thermistor) sensor, anultrasonic sensor, a capacitive sensor, a micropower impulse radarsensor, a global positioning satellite (GPS) sensor, an altimeter (e.g.,to detect which floor of a building the detector has been placed, basedon altitude), mmWave, and the like. Structured light involves projectinga known pattern (e.g., a grid or horizontal bars) of light on to an area(e.g., detection zone). The way in which the light deforms when strikingthe area enables a vision system (e.g., imaging sensor(s) and software)to determine the depth and surface information associated with a pest inthe area. An mmWave sensor is able to detect objects (e.g., pests) andprovide a range, a velocity, and an angle of each of the objects. Anmmwave sensor operates in the spectrum between 30 GHz and 300 GHz.

The pest detector may have one or more sensors that monitor a particulararea (e.g., detection zone) or set of (e.g., one or more) areas. When asensor (e.g., motion detector, infrared imaging sensor, or the like)detects motion associated with a potential pest in the detection zone,sensor data, such as an image of the potential pest, may be captured.For example, an ambient light sensor (ALS) may detect an amount oflight. If the ALS detects that the amount of available light satisfies alight threshold, then an imaging sensor may be used to capture a digitalimage (or a set of digital images=a video) of the potential pest. If theALS detects that the amount of available light does not satisfy thelight threshold, then either an infrared imaging sensor may be used tocapture a digital image of the potential pest or a light emitting diode(LED) may be used as a flash to briefly illuminate the potential pest toenable an imaging sensor to capture a digital image.

The pest detector may use the gathered data (e.g., digital image,movement information, and the like) associated with the potential pestto determine whether the data indicates a pest and if so, identify thepest using a machine learning (ML) algorithm. The ML algorithm may use,for example, a support vector machine or other type of classifier,clustering, Bayesian network, reinforcement learning, representationlearning, similarity and metric learning, sparse dictionary learning,rule-based machine learning, or the like. The ML may be trained torecognize multiple types of pests and to ignore data indicative ofhumans or pets (e.g., dogs and cats). In some cases, the type of peststhat the ML can recognize may be based on the geographic region in whichthe pest detector is placed. For example, the pest detector maydetermine a location of the pest detector and download data associatedwith pests found in a region that encompasses the detector's location.To illustrate, a pest detector located in the southwestern United Statesmay download an ML capable of detecting scorpions and snakes that arecommon to the local region, in addition to detecting ants, cockroaches,mice, rats, and other pests that are common to all geographic areas.

The ALS may be used to transition the detector to and from a low powermode. For example, many pests tend to be active during darkness. Inaddition, a human being is likely to detect pests that are active whenthere is at least a threshold amount of light by seeing or hearing thepests or effects of their activity. Thus, the detector may be in adetection mode when the light measured by the ALS satisfies a lightthreshold (or the presence of a human is detected). The detector maytransition from the detection mode to a low power mode when the ALSdetermines that the light does not satisfy the light threshold (or apresence of a human is not detected for more than a threshold amount oftime). When the ALS determines that the light satisfies the threshold(or detects the presence of a human), the detector may transition fromthe low power mode to the detection mode. For example, if the detectoris powered using a battery, then transitioning to the low power mode mayconserve battery power and enable the detector to function for a longertime using one or more batteries as compared to if the low power modewas not used. Detectors that draw power from an electrical outlet (e.g.,alternating current (A/C)) may, in some cases, not use the low powermode. The low power mode may be a user selectable option such that auser can select to disable low power mode, thereby causing the detectorto remain in detection mode.

The detector may be available in different models, such as a batterypowered model and an A/C powered model. The detector models may includea weather sealed model (e.g., that uses gaskets) to prevent moisture andparticulate matter from entering into a housing of the detector toenable the detector to be used outdoors to detect outdoor pests (e.g.,rabbits, squirrels, raccoons, snakes, wasps, and the like). The detectormodels may include a modular model that enables sensor modules to beattached to (and detached from) the housing of the detector. Forexample, if the detector is approximately cube-shaped, a sensor modulethat includes one or more sensors may be attached (e.g., plugged into)one or more of the six surfaces of the cube to enable the detector tomonitor multiple detection zones. The detector models may include adetector with a back-facing sensor in which, after the detector isplugged into a wall outlet for A/C power or a battery powered unit isattached to the wall, the sensor faces the wall and is able to detectpests (e.g., mice, termites, carpenter ants, and the like) inside thewall. For example, the back-facing sensor may use ultrasonic,capacitive, or micropower impulse radar to detect the shapes andmovements of pests within the wall. The back-facing sensor may bebuilt-in to the detector or into an extendable housing to enable thesensor to be positioned over a particular area of the wall (e.g., wherepest activity is suspected due to noises or other pest effects). Aplug-in module may include bait and two small electrodes to lure andkill (e.g., using electrocution) a small pest, such as a mosquito, afly, an ant, or small cockroach, or another type of pest. The detectormodels may include a detector with a swiveling A/C plug to enable a userto position the detection area to be below the detector (e.g., detectionzone includes the area where the wall meets the floor), above thedetector (e.g., detection zone includes the area where the wall meetsthe ceiling), or one side of the detector (e.g., detection zone includesa portion of the wall to one side of the detector). In some cases, thedetector may be incorporated into an A/C wall outlet that can beinstalled in a new house or as a replacement in an existing house. Suchan implementation may provide one or two A/C receptacles and expose oneor more sensors through a cover plate.

Each detector may include one or more external indicator lights tovisually indicate a mode (e.g., detection mode or low-power mode),network connectivity (e.g., connected to or disconnected from network),pest detection (e.g., green indicates no pests detected, red indicates apest was detected), and other information associated with the detector.In some cases, different colored covers may be used to enable thedetectors to blend in to a particular décor. For example, a blue covermay be snapped on to a detector for a boy's room and a pink cover may besnapped on to a detector for a girl's room. A corporate logo or agraphic (e.g., associated with Disney®, Star Wars®, Marvel®, or thelike) may be embossed or silk-screened on the cover.

Each detector may include a wireless network interface (e.g., WiFi®,Bluetooth®, or the like) to enable the detector to communicate with (i)other detectors, (ii) an application (“app”) executing on a user'scomputing device, (iv) a cloud-based server, (iii) a pest servicescompany, or any combination thereof. For example, the detector maycreate a mesh network with other detectors using a short distancenetworking protocol, such as, for example, Bluetooth®, ZigBee, or thelike. As another example, the detector may communicate with otherdetectors, one or more user devices, a server, or other devices usingWiFi® or another type of wireless networking technology. The detectormay communicate data to an application executing on a user device, suchas a smartphone, a tablet, or a virtual assistant enabled device (e.g.,Amazon® Echo® or Alexa®, Google® Home, Apple® Homepod, or the like).

An app (created by a manufacturer of the detector) may be downloaded andinstalled on a user device, such as a computing device associated withan occupant of a home, a warehouse staff member, a pest control service,or the like. The app may display a user interface (UI) to display datareceived from multiple detectors in a particular location, such as ahouse, a warehouse, an industrial plant, or another type of building orset of buildings. For example, the UI may display an approximate floorplan of the particular location and an approximate location of eachdetector within the floor plan. The UI may display data associated witheach detector, such as a mode (e.g., detection mode or low-power mode),network connectivity (e.g., connected to or disconnected from anetwork), whether or not the detector has detected a pest (e.g., greenindicates no pests detected, red indicates one or more pests weredetected), and other information associated with the detector. The UImay display one or more predictions and/or suggestions made by the MLalgorithm. For example, the predictions may include that a particulartype of pest appears to nesting in a particular location. To illustrate,the multiple detectors in the location may, after detecting a pest,determine a direction in which the pest is travelling and predict wherethe pests are nesting (e.g., breeding) based on the direction data. TheML may make suggestions such as “add a detector in this room and on thiswall” to provide additional data, “add a detector with a back-facingsensor in this location” to determine (e.g., confirm) whether a pest isnesting behind a particular wall, and the like. The UI may enable a userto view the data gathered by each detector, such as a digital image ofthe pest captured by the detector. The UI may provide information as tonearby (e.g., within a predetermined radius from a current location ofthe device on which the app is installed) pest control service providersand enable the user to request a quote for pest control services.

The data gathered by each detector (e.g., pest-related data) may be sentto a server (e.g., a cloud-based server). The server may thus receivedata from multiple detectors in each of multiple locations (e.g.,houses, warehouses, industrial plants, restaurants, grocery stores, andthe like). The server may execute a second ML algorithm to provideadditional analysis and prediction. For example, if multiple locationsin a particular neighborhood of a city detect a particular pest, theserver may proactively provide a suggestion (via the UI of the app) tousers located in the particular neighborhood, e.g., “This particularpest has been detected in your neighborhood but has not yet beendetected in your location (e.g., house). We recommend taking thefollowing preventative measures to prevent this pest from becoming aproblem.” In addition, if the detector is unable to identify aparticular pest, the detector may send the data (e.g., digital image,audio recording, video recording, movement data, structured light data)associated with the particular pest to the server for further analysis.The server may determine the type of pest that the detector detected andsend an update to the detector. For example, if a new pest is detected,the server may send an updated ML algorithm or an updated (or new) pestprofile to detectors located in a same region. For example, if aparticular region experiences unseasonably warm weather that causes alarge number of a particular pest (e.g., locusts, crickets, or the like)to breed and the current version of the ML in the deployed detectors isnot capable of detecting the particular pest, then after one or moredetectors send pest-related data to the server, the server's MLalgorithm may identify the pest, create an updated detector MLalgorithm, and send (or provide for download) the updated ML algorithmto detectors in the particular region. Each detector may install theupdated ML algorithm to enable each detector to detect the particularpest. In this way, if a new pest becomes prevalent in a particularregion, the new pest can be detected by updating the ML algorithm usedby each detector.

In some cases, a pest control service provider may rent the detectors tohomeowners or commercial users at no charge or a nominal charge inexchange for providing pest control services to the location in whichthe detectors are deployed. For example, a homeowner may suspect that aparticular pest (e.g., rats) are in a particular room (e.g., attic) andcontact the pest control service provider who sends a servicerepresentative to place multiple detectors in the homeowner's house. Thedetectors may send data associated with detected pests to the pestcontrol service provider who then presents a list of pests detected andan estimate to treat the house to rid the house of the detected pests.In other cases, a homeowner, landlord, company, or other entity maypurchase and deploy the detectors in one or more buildings. When one ormore pests are detected, the app may be used to request quotes frommultiple pest control service providers.

For example, a pest detector may include one or more sensors, one ormore processors, and computer-readable storage media to storeinstructions executable by the one or more processors to perform variousoperations. The operations may include receiving sensor data from one ormore sensors. The sensor data may include a set of (e.g., one or more)digital images, a digital audio recording, or both. For example, amotion sensor may detect movement associated with a pest and an imagingsensor may capture one or more digital images of the pest. The one ormore sensors may include at least one of: a motion sensor, an imagingsensor, a microphone, a structured light sensor, an ultrasound sensor, atemperature sensor, an ultrasonic sensor, a capacitive sensor, or amicropower impulse radar sensor. The operations may include using amachine learning algorithm to determine that the sensor data indicates apresence of a pest, and sending a notification message to a computingdevice. The machine learning algorithm may determine a type of the pest.For example, the machine learning algorithm may determine whether thepest is a cockroach, a mouse, or a rat. The notification message mayinclude at least a portion of the sensor data (e.g., a digital image ofthe pest). The operations may include visually indicating, using anexternal indicator light of the detector, that the pest was detected.The operations may include storing the sensor data in a memory of thedetector to create stored data and sending the stored data to a server.The operations may include receiving ambient light data from an ambientlight sensor of the detector, determining that the ambient light datasatisfies a predetermined threshold, and transitioning the detector froman active mode to a low-power mode. A particular sensor of the one ormore sensors may include an ultrasonic sensor, a capacitive sensor, or amicropower impulse radar sensor, e.g., a sensor capable of detectingmovement within a wall.

A sensor plugin may include at least one sensor of the one or moresensors and a plug to mate with a receptacle in a housing of thedetector. The plug may provide a mechanical linkage and electricallinkage to the detector. For example, the electrical linkage may carrypower from the detector to the at least one sensor and carry additionalsensor data from the at least one sensor to the one or more processorsin the detector. An electrified plugin may include a shallow receptaclein which bait is placed and an electrified mesh to electrocute aparticular pest that is attracted by the bait. The sensor plugin mayinclude a telescoping extender that can be extended to position the atleast one sensor at a particular location to create a pest detectionzone. For example, the at least one sensor may face backward to detectpest movement within a wall.

The operations may include receiving second sensor data from the one ormore sensors, determining that the machine learning algorithm does notrecognize a second pest in the second sensor data, and sending a messagethat includes the second sensor data to a server requesting assistanceidentifying the second pest. In response, the detector may receive anupdate to the machine learning algorithm from the server, install theupdate to create an updated machine learning algorithm, and determining,by the updated machine learning algorithm, that the second sensor dataindicates the presence of the second pest.

FIG. 1 is a block diagram illustrating a detector 100, according to someembodiments. The detector 100 may be cube-shaped with six surfaces,including a front 102, sides 104(1), 104(2), 104(3), 104(4), and a back106. Of course, the detector 100 may, in some cases, have another typeof geometric shape or non-geometric shape.

One or more sensors 108(1) to 108(N) (where N>0) may be dispersed acrossone or more of the surfaces 102, 104, or 106. The sensors 108 may, forexample, include a motion sensor, an imaging sensor (e.g., a camera), anaudio transducer (e.g., microphone), a structured light sensor, anultrasound sensor, an infrared imaging sensor, a temperature (e.g., athermistor) sensor, an ambient light sensor (ALS), another type ofsensor, or any combination thereof. For example, a motion sensor maydetect the presence of a pest and cause (e.g., trigger) an imagingsensor to capture one or more digital images of the pest. To illustrate,the imaging sensor may capture multiple digital images at a particularrate, such as X frames per second (where X>0). The multiple digitalimages may be used by a machine learning algorithm of the detector 100to determine (e.g., predict) a direction in which the pest is going. Bydetermining the direction of movement of multiple pests detected bymultiple detectors, the detectors may determine (e.g., predict) wherethe pests are likely nesting (e.g., breeding) or congregating. In somecases, the rate of movement of the pest (or potential pest), e.g.,movement between a first image captured at a first point in time and asecond image captured at a second point in time, may be used todetermine the type of pest.

The detector 100 may include one or more indicators 110. The indicatorlights may visually indicate (i) a mode (e.g., detection mode orlow-power mode) of the detector 100, (ii) a network connectivity (e.g.,connected to or disconnected from network) of the detector 100, (iii)whether a pest was detected (e.g., green indicates no pests detected,red indicates a pest was detected), (iv) whether an update is beinginstalled, (v) other information associated with the detector 100, orany combination thereof.

FIG. 2 is a block diagram 200 illustrating connecting a detector capableof accepting modular plugins, according to some embodiments. One or moreof the surfaces 102, 104, 106 of the detector 100 may include areceptacle 202 to accept a plug 204 of a plugin 206(1). For example, theside 104(1) may include a receptacle 202(1), the side 104(4) may includea receptacle 202(2), and the front 102 may include a receptacle 202(3).

The plugin 206(1) may include one or more of the sensors 108 (e.g., amotion detector sensor and an imaging sensor). In this way, a user canattach various plugins to cover detection zones appropriate for aparticular environment. In some cases, one type of plugin with a firstset of sensors may be used to detect pests that travel along where thefloor meets the wall while another type of plugin with a second set ofsensors may be used to detect pests that travel along where the wallmeets the ceiling, and the like.

A plugin 206(2) may include bait 208 and an electrified mesh 210. Thebait 208 may be a type of pest attractant that is used to lure a pesttowards the plugin 206(2). When the pest touches the electrified mesh210, the pest may be killed (e.g., electrocuted) by supplying power(e.g., voltage and current) to the electrified mesh 210. In some cases,the electrified mesh 210 may be provided power without regard to whethera pest is detected. In other cases, the plugin 206(2) may include asensor, such as a motion sensor. When the motion sensor detectsmovement, the detector 100 may supply power to the electrified mesh 210.When the motion sensor no longer detects movement, the detector 100 maystop supplying power to the electrified mesh 210. In some cases, thebait 208 may be used without the electrified mesh 210 to attract peststo come near the detector 100 to enable the detector 100 to gathersensor data from the sensors 108.

The plug 204 may serve two purposes. First, the plug 204 may be used to(temporarily) secure the plugin 206(1), 206(2) to the detector 100.Second, the plug 204 may include electrical contacts to enable thedetector 100 to provide power to the sensors 108 in the plugin 206(1)and to the electrified mesh 210 of the plugin 210(2). In some cases,power may also be provided to the bait 208. For example, the pestattractant may be vaporized by applying power to the bait 208.

FIG. 3 is a block diagram 300 illustrating detection zones of a detectoraccording to some embodiments. The detector 100 may be placed on a wall302 with a first set of (e.g., one or more) sensors 304(1) facing down(e.g., towards where a floor meets the wall 302) and a second set ofsensors 304(2) facing up (e.g., towards where the wall 302 meets aceiling). The set of sensors 304(1) may create a detection zone 306(1)and the set of sensors 304(2) may create a detection zone 306(2).

FIG. 4 is a block diagram 400 illustrating a detector to detect movementwithin a wall, according to some embodiments. The detector 100 may be(temporarily) attached to the wall 302 using an attachment mechanism402. For example, the attachment mechanism 402 may include pluggingelectrical prongs protruding from the back 106 of the detector 100 intoan electrical outlet on an outside 404 of the wall 302. As anotherexample, the attachment mechanism 402 may use double-sided tape, hookand loop (e.g., Velcro®) strips, or non-damaging glue strips (e.g., 3M®Command®) to attach the back 106 of the detector 100 to the outside 404of the wall 302. One ore more of the sensors 108, such as therepresentative sensor 108(1), may face the back 106 of the detector 100and may be capable of detecting movement of pests on an inside 406 ofthe wall 302.

In some cases, a housing 408 that includes one or more additionalsensors 108, such as the representative sensor 108(2), may be attachedto an extender 410. The extender 410 may be a fixed length or mayinclude a variable length (e.g., telescoping) mechanism to enable thesensor 108(2) to be positioned at a particular location on the outside404 of the wall 302. By positioning the housing 408 appropriately, thesensor 108(2) may detect the movement of pests (e.g., rats, mice,termites, carpenter ants, and the like) on the inside 406 of the wall302, e.g., the detection zone may include the inside 406 of the wall302. The extender 410 may be permanently attached to the detector 100 ormay be attached to the detector 100 using the mechanism described inFIG. 2. For example, the bottom of the extender 410 may include the plug204 that can be attached to the receptacle 202(1) in the detector 100.

FIG. 5 is a block diagram illustrating a detector that includes a swivelplug, according to some embodiments. In some cases, the back 106 of thedetector 100 may include an alternating current (A/C) plug 502 that maybe plugged into an A/C wall outlet to obtain power. The A/C plug 502may, in some cases, be mounted using a swivel mechanism 504. Forexample, if the detector 100 includes a fixed set of sensors on one ofthe sides 104 (e.g., as illustrated in FIG. 1), then the swivelmechanism 504 may enable the detector 100 to be swiveled to position thesensors to create a desired detection zone. For example, if the userdesires that the detection zone include where the floor meets the wall,then the detector 100 may be rotated until the sensors are facingdownwards (e.g., similar to the sensors 304(1) in FIG. 3). If the userdesires that the detection zone include where the ceiling meets thewall, then the detector 100 may be rotated until the sensors are facingupwards (e.g., similar to the sensors 304(2) in FIG. 3). In some cases,the detector 100 may include an electrical mount (e.g., screw mount orplug mount) instead of the A/C plug 502 to enable the detector 100 to bemounted into a light socket. The detector 100 may include a pass-throughlight socket (e.g., on the front 102) to enable a light to be attachedto and powered via the detector 100. Sensors located on the front 102,one or more of the sides 104, and the back 106 may enable a near 360degree detection area.

FIG. 6 is a block diagram of a system 600 that includes multipledetectors connected to a network, according to some embodiments. In thesystem 100, multiple detectors, such as the representative detector 100,may be coupled to one or more servers 602, and one or more computingdevices 628(1) to 628(M) (M>0), via one or more networks 106. Forexample, the computing device 628(1) may be a user device, such as asmart phone, tablet, or voice-assistant enabled device. The computingdevice 628(M) may be a device associated with a pest control servicescompany. For example, when pests are detected, the pest control servicescompany may contact an owner or manager of a property, indicate thatpests were detected, and provide an estimate to treat the property forthe pests.

The representative detector 100 may include the sensors 108(1) to108(N), one or more processors 604, a communications interface 606, amemory 608, and a power source 610. The processors 604 may includecustom logic devices or off-the-shelf processors that use a design by acompany such as Intel®, AMD®, ARM®, or the like. The communicationsinterface 606 may be capable of communications using one or more wiredor wireless protocols, such as, for example, Ethernet®, Wi-Fi®, ZigBee®,Bluetooth®, another type of communications protocol, or any combinationthereof. For example, the communications interface 606 may be capable of(1) creating a mesh network with other detectors, (2) communicating witha user device that is executing an application 634 (e.g., as illustratedin FIGS. 7 and 8) associated with the detector 100, (3) communicatingwith the servers 602, or any combination thereof. The memory 608 mayinclude any type of non-transitory computer-readable storage media,including random access memory (RAM), solid state drive (SSD), removablememory (e.g., Secure Digital (SD) or micro SD), or the like. The powersource 610 may be capable of converting A/C power to direct current (DC)and may enable the detector 100 to be plugged into an A/C wall outlet oruse a battery source for power. In some cases, the power source 610 mayinclude a rechargeable battery that receives a trickle charge from thepower source 610. In this way, the detector 100 may remain active (e.g.,by using power from the rechargeable battery) when the A/C power istemporarily unavailable (e.g., due to a brownout or other issue).

The memory 608 may be used to store software applications and data. Forexample, the memory 608 may store a machine learning algorithm (ML) 612that has been trained to recognize shapes, sounds, or other sensor dataassociated with a particular set of pests. For example, if the detector100 is intended for indoor use, the ML 612 may be trained to recognizeindoor pests (e.g., ants, cockroaches, silverfish, and the like)associated with a particular geographic region. If the detector 100 isintended for outdoor use, the ML 612 may be trained to recognize outdoorpests (e.g., wasps, snakes, raccoons, and the like) associated with aparticular geographic region. In some cases, the manufacturer of thedetector 100 may charge a fee to download updated machine learningalgorithm. For example, if a user moves from one geographic region toanother, the user may pay a fee and download a machine learningalgorithm trained to detect pests in the new geographic region. When oneof the sensors 108 (e.g., motion detector) detects a pest, additionalones of the sensors 108 may capture sensor data 632 (e.g., image data,audio data, and the like) associated with the pest. The ML 612 mayanalyze the sensor data 632 to determine a type of the pest. Forexample, the ML 612 may identify a digital image in the sensor data 632to match an image of a cockroach, a mouse, a rat, a snake, a scorpion,or another type of pest. As another example, the ML 612 may determinethat audio data included in the sensor data 632 matches that of a mouse,a rat, termites, a squirrel, or another type of pest. In some cases, thesoftware 616 may compare the sensor data 632 with the stored data 614 todetermine if the currently detected pest was previously detected. Inthis way, the detector 100 may determine the number of a particularpest. For example, the detector 100 may determine that there are atleast three different cockroaches based on the size and/or shape of eachcockroach. As another example, if the same mouse goes back and forthpast the detector 100 multiple times, the detector 100 may determinethat there is a single mouse and not many mice.

The detector 100 may receive data (e.g., digital image files, audiofiles, motion data, and the like) from the sensors 108 and store thedata to create stored data 614. The stored data 614 may be stored in afirst in first out (FIFO) circular buffer, with older data beingoverwritten by newer data. The memory 608 may store software 616. Forexample, the software 616 may receive ambient light data from an ALS (ofthe sensors 108) and determine whether to transition the detector 100from low power mode to active mode (e.g., when the data satisfies apredetermined threshold, e.g., indicating that the detector's locationis relatively dark) or from active mode to low power mode (e.g., whenthe data does not satisfy the predetermined threshold, e.g., indicatingthat the detector's location is relatively well lit). The software 616may indicate which mode the detector 100 is currently in using one ofthe indicators 110 (e.g., green=active, yellow=standby). The software616 may determine whether the detector 100 is connected to the network106 using the communications interface 606 and display the connectionstatus using one of the indicators 110 (e.g., green=connected,red=disconnected). The software 616 may use one of the indicators 100 toindicate whether the detector 100 is being powered by A/C power orbattery power (e.g., green=A/C, yellow=battery). The software 616 mayuse one of the indicators 100 to indicate whether the detector 100 hasdetected a pest (e.g., green=no pests detected, red=pest detected).

After the detector 100 detects a pest, the software 616 may send anotification 624 to one or more of the computing devices 628 (e.g., fordisplay in a UI of the app 634). The software 616 may send data 626,such as sensor data received from one or more of the sensors 108, to thecomputing devices 628. In some cases, the notification message 624 mayinclude the sensor data 632 (e.g., the data 626). For example, thecomputing device 628(1) may receive the notification 624 and provide anaudible and/or visual indication that a pest detection notification wasreceived. The user may open the UI of the app 634 on the computingdevice 628(1). The computing device 628(1) may receive and display thedata 626 (e.g., one or more digital images captured by the sensors 108).Receiving the notification 624 may cause a computing device, such as thecomputing device 628(1), to automatically (e.g., without humaninteraction) launch the app 634 and automatically display thenotification 624 (and display the data 626) within a user interface (UI)of the app 634.

The detector 100 may, in response to detecting one or more conditions,send at least a portion of the stored data 614 to the server 602 as thedata 626. To illustrate, the detector 100 may send the data 626 inresponse to detecting a pest. The detector 100 may send the data 626 inresponse to detecting a pest that the ML 612 is currently incapable ofrecognizing, causing the server 602 to identify the pest and, in somecases, send an updated ML 612. The detector 100 may send the data 626 inresponse to determining that a size of the stored data 614 satisfies apredetermined threshold (e.g., the stored data 614 is occupying at leastY % of the capacity of the memory 608, where 100>Y>0). The detector 100may send the data 626 after a predetermined period of time (e.g., a day,a week, a month, or the like) has elapsed, at a time when the network106 is relatively unused, such as, for example, 2:00 AM.

The servers 602 may be hardware servers, cloud-based servers, or acombination of both. The servers 602 may store a remote ML algorithm 618and a database 620. The remote ML 618 may be much larger and moresophisticated and may be capable of recognizing many more pests than theML 612 used by the detector 100. The server 602 may receive and storethe data 626 in the database 620. The database 620 may store datareceived from multiple detectors deployed in multiple geographic regionsover a long period of time. In contrast, the stored data 614 in thedetector 100 may have a limited size and may store data acquired over arelatively short period of time. If the data 626 indicates that thedetector 100 was unable to recognize the pest, the data 626 may be addedto the database 620 and the remote ML 618 may retrain the machinelearning algorithm (e.g., used by a detector) using at least a portionof the database 620 to create a new detector ML 622. The server 602 maysend an update 630 that includes the new detector ML 622 to one or moredetectors via the network 106. For example, if the server 602 determinesthat a particular pest that was relatively absent has now becomeprevalent in a particular geographic region, the server 602 may createand send the new detector ML 622 to detectors located in the particulargeographic region. In this way, the server 602 may continually provideupdate the detectors 100 to detect new and evolving pests (e.g., biggermice, smaller cockroaches). The remote ML 618 may perform an analysis636 of the data 626 received from detectors located in a particularstructure (e.g., detectors in the same house, warehouse, industrialplant, restaurant, apartment building, or the like) and provide theanalysis 636 to the computing devices 628. The app 634 may display theanalysis 636, including predictions pertaining to the detected pests.The sensors 108 may include a temperature sensitive sensor, such as, forexample, a thermistor and a humidity sensor (e.g., using capacitive,resistive, or thermal conductivity technology). The temperaturesensitive sensor may capture temperature data and the humidity sensormay capture humidity data and send the captured data to the ML 612. Theremote ML 618 may be trained to consider temperature and humidity andmake predictions based on the temperature data and the humidity data.For example, for detectors that are placed outside, the remote ML 618may make predictions based on current weather conditions, includingtemperature, humidity, and weather forecasts e.g., “Scorpions arepredicted because the temperature is greater than X degrees”, “Cricketsare predicted because the temperature is greater than X degrees and thehumidity is less than Y”, and so on. The predictions may be based on (1)previous data gathered under similar conditions (e.g., temperature X,humidity Y for Z length of time usually cause the number of cockroachesto increase significantly) and (2) data gathered from detectors locatednearby (e.g., several of your neighbors have experienced an increase inant activity in the past few days). The server 602 may aggregate datafrom multiple detectors deployed in multiple locations (e.g., houses orbuildings) and make predictions. For example, increased activity inmultiple buildings that are in close proximity to each other may causethe remote machine learning 618 to predict a large scale infestationspanning the multiple buildings.

In some cases, if there are multiple detectors deployed in a building,the detectors may create a mesh network. The server 602 may send theupdate 630 to one of the multiple detectors and instruct the detectorthat receives the update 630 to share the update 630 with the otherdetectors in the building, via the mesh network.

FIG. 7 is a block diagram 700 illustrating a user interface to displaylocations of detectors in a floor plan, according to some embodiments. Acomputing device, such as one of the computing devices 628 of FIG. 6,may execute an application (e.g., the app 634 of FIG. 6) provided by amanufacturer of the detector 100. The application may display a userinterface (UI) 702 that includes a floor plan 704 for a property orlocation.

In some cases, the application 634 of FIG. 6 may send the floor plan 704to the server 602 and the remote machine learning 618 may determinelocations where multiple detectors 706(1) to 706(P) are to be placed. Ahomeowner may pay for a service that uses the remote machine learning618 to determine in which locations the detectors 706 are to be placed.The server 602 may send data to one of the computing devices 628 (e.g.,the homeowner's device) to enable the UI 702 to display the floor plan704 and superimpose the locations on the floor plan 704. The homeownermay deploy the detectors 706 according to the locations specified in thefloor plan 704. Alternately, a commercial pest control service that hasa service agreement with an owner of the server 602 may measure therooms in a house, create the floor plan 704, and send the floor plan tothe server 602. The remote machine learning 618 may determine thelocations in which the detectors 706 are to be placed and send data toone of the computing devices 628 (e.g., a device of the pest controlservice) to enable the UI 702 to display the floor plan 704 andsuperimpose the locations on the floor plan 704. An employee of the pestcontrol service may deploy the detectors 706 based on the locationssuperimposed on the floor plan 704.

The UI 702 may display the detectors 706 deployed within an areadisplayed by the floor plan 704 and a corresponding status 708corresponding to each of the detectors 706. The information displayed bythe status 706 is described in more detail in FIG. 8. The floor plan 704may displays one or more rooms 710(1) to 710(P) and provide anindication as to approximately where each of the detectors 706(1) to706(P) are located. The UI 702 may provide a visual indicatoridentifying a detector that recently detected a pest. For example, inFIG. 7, the UI 702 visually indicates that the detector 706(1) recentlydetected a pest. The UI 702 may display arrows (e.g., see rooms 710(2),710(4), 710(6), and 710(7)) indicating a direction in which pestmovement has been detected by the detectors 706.

While the floor plan 704 illustrated in FIG. 7 is of a single-familyhome, other floor plans may also be displayed by the UI 702. Forexample, the UI 702 may include a floor selector 712 to enable a user toselect a particular floor of multiple floors of a building (a house,apartment, or commercial building), a building selector 714 to enable auser to select a particular building of multiple buildings (e.g., aparticular warehouse of multiple warehouses), and the like. In somecases, the UI 702 may enable a user to zoom in and out, e.g., zoom outto view multiple buildings having multiple floors, zoom into aparticular building, a particular floor, and a particular room of theparticular floor.

The UI 702 may display one or more predictions 716 (e.g., included inthe analysis 636) determined by the remote ML 618. The predictions 716may be based on correlating the individual data (e.g., the data 626 ofFIG. 6) sent from individual ones of the detectors 706(1) to 706(P) tothe server 104. The predictions 716 may include, for example,“Cockroaches appear to be gathering underneath the dishwasher,refrigerator, and stove in room 710(1)”. The UI 702 may display one ormore suggestions 718 (e.g., determined by the remote ML 618 and includedin the analysis 636), such as, for example, “Consider moving detector706(6) from room 710(6) to bathroom #1 as pest movement data indicatesthat pests may be moving to that area”. The predictions 716 and thesuggestions 718 may be determined by the remote ML 618 of FIG. 6. For acommercial pest control service, the suggestions 718 may includesuggestions to re-deploy at least some of the detectors from a firsthouse associated with a first customer to a second house associated witha second customer. For example, the detectors in the first house maydetermine that pest activity has decreased (e.g., pest activity rarelydetected) while the detectors in the second house may determine thatpest activity has increased. By selectively redeploying pest detectorsthat the pest control service has already acquired based on the remotemachine learning algorithm, the pest control service may avoid acquiringadditional pest detectors and instead redeploy pest detectors based onthe amount of activity detected in each house.

FIG. 8 is a block diagram 800 illustrating a user interface to displaydata gathered by a detector, according to some embodiments. The UI 702may visually display the detectors 710(1) to 710(P) and theircorresponding status. The user may select a particular detector, such asthe detector 710(1). In response, the UI 702 may display the correspondstatus, such as the status 708(1).

The status 708(1) may include a power status 802, a mode 804, a networkconnection status 806, and a pest detected status 808. The power status802 may indicate whether the detector 710 is being powered by A/C poweror battery power (e.g., green=A/C, yellow=battery). The mode 804 mayindicate a mode (e.g., active mode or low power mode) of the detector710 (e.g., green=active, yellow=standby). The network connection status806 may indicate whether the detector 710 is connected to a network(e.g., green=connected, red=disconnected). The pest detected status 808may indicate whether the detector 710 has detected a pest (e.g.,green=no pests detected, red=pest detected). If at least one pest wasdetected, the pest detected 808 may display each type of pest 810(1) to810(Q) (Q>0) that was detected and the associated sensor data 812(1) to812(Q). For example, if three different sized cockroaches were detected,an image of each of the three may be displayed. As another example, ifmice were detected behind a wall, an ultrasound or other type of imagemay be displayed and an audio recording of the noises made by the micemay be made available for playback.

When the UI 702 receives a notification and sensor data from a detector(e.g., the notification 624 and the data 626 from the detector 100), theUI 702 may visually indicate that the detector (e.g., the detector710(1)) has detected a pest, display the type of the pest 810 and thecorresponding sensor data 812.

The UI 702 may display pest control service provider information 814,such as provider names 816(1) to 816(R) (R>0) and corresponding providerinformation 818 (e.g., address, phone number, email address, distancefrom the device displaying the UI 702). The UI 702 may provide aselection to enable the user to request a quote 820 from one of thecorresponding providers 816. For example, selecting the request quote820(1) selector may cause the UI 702 to initiate a voice call (or sendan email) to a phone number (or email address) associated with theprovider name 816(1) to request a quote.

In the flow diagrams of FIGS. 9 and 10, each block represents one ormore operations that can be implemented in hardware, software, or acombination thereof. In the context of software, the blocks representcomputer-executable instructions that, when executed by one or moreprocessors, cause the processors to perform the recited operations.Generally, computer-executable instructions include routines, programs,objects, modules, components, data structures, and the like that performparticular functions or implement particular abstract data types. Theorder in which the blocks are described is not intended to be construedas a limitation, and any number of the described operations can becombined in any order and/or in parallel to implement the processes. Fordiscussion purposes, the processes 900 and 1000 are described withreference to FIGS. 1, 2, 3, 4, 5, 6, 7, and 8 as described above,although other models, frameworks, systems and environments may be usedto implement these processes.

FIG. 9 is a flowchart of a process 900 that includes identifying a typeof pest using sensor data, according to some embodiments. The process900 may be performed by one or more components of a detector, such asthe detector 100 of FIGS. 1-6.

At 902, sensor data may be received from one or more sensors. At 904, adetermination may be made whether the sensor data indicates a presenceof a pest. If a determination is made, at 904, that “no” the sensor datadoes not indicate the presence of a pest, then the process may proceedback to 902. If a determination is made, at 904, that “yes” the sensordata indicates the presence of a pest, then the process may proceed to906, where a type of the pest may be determined. For example, in FIG. 6,the sensors 108 may detect the presence of a pest and capture the sensordata 632 associated with the pest. The sensors 108 may send the sensordata 632 to the processors 604. The processors 604 may determine whetherthe sensor data 632 indicates the presence of a pest. For example, ifthe sensor data 632 indicates the presence of a human or a pet (e.g.,cat or dog), then the sensor data 632 may be discarded and theprocessors 604 may wait to receive additional sensor data. If the sensordata 632 indicates the presence of a pest, then the process maydetermine, using the ML 612, a type of the pest. For example, the ML 612may be trained to recognize (e.g., classify or predict) one of multipletypes of pests based on the sensor data 632.

At 908, a notification may be sent (e.g., to a computing device). Forexample, in FIG. 6, the detector 100 may send the notification 624indicating that a particular type of pest was detected to one or more ofthe computing devices 628. The UI 702 may provide an audible (and/orvisual indication) that the notification 624 was received.

At 910, the sensor data may be sent (e.g., to the computing device andto a server). For example, in FIG. 6, the detector 100 may send the data626 (e.g., including the sensor data 632) to one or more of thecomputing devices 628, to the server 602, or any combination thereof.For example, the UI 702 may display the data 626. The server 602 maystore the data 626 in the database 620. In some cases, the server 602may analyze the data 626 to determine additional information, such ashow many humans live in a house, how many adults live in the house, howmany children live in the house, how many pets live in the house, howmuch time the humans spend in each room that has a detector, and thelike. Such an analysis may be used to provide targeted advertising tothe occupants of the house.

At 912, an update may be received from the server. At 914, the updatemay be installed. For example, if the detector 100 could not identify aparticular pest, the detector 100 may send the sensor data 632 to theserver 602. The remote ML 618 may identify the pest, retrain the MLalgorithm using the database 620 to create the new detector ML 622, andsend the update 630 that includes the new detector ML 622 to one or moredetectors, including the detector 100. The detector 100 may install theupdate 630 to enable the detector 100 to detect the previouslyunidentifiable pest.

FIG. 10 is a flowchart of a process 1000 that includes displaying one ormore predictions regarding pests, according to some embodiments. Theprocess 1000 may be performed by an application executing on a computingdevice, such as the app 634 executing on the computing devices 628 ofFIG. 6.

At 1002, a user interface (UI) identifying one or more pest detectorsmay be displayed. At 1004, the UI may display an approximate layout(e.g., floor plan) of a structure. At 1006, the UI may display locationsof the one or more pest detectors superimposed on the layout of thestructure. At 1008, the UI may display a status of each pest detector.For example, in FIG. 7, the UI 702 may display the detectors 706 and acorresponding status 708. The UI 702 may display approximate locationsof each of the detectors 706 superimposed on the floor plan 704. The UI702 may visually indicate which of the detectors 706, such as thedetector 706(1), has detected a pest.

At 1010, the UI may display one or more predictions associated with thepests that were detected. At 1012, the UI may display one or moresuggestions. For example, in FIG. 6, the remote ML 618 may analyze atleast a portion of the data stored in the database 620 to create theanalysis 636. The server 602 may send the analysis 636 to the computingdevices 628 for display by the app 634. The analysis 636 may include thepredictions 716 and the suggestions 718 of FIG. 7. The predictions 716may include predictions on particular location(s) where pests arepredicted to be nesting based on pest movement.

At 1014, the UI may receive a notification message and sensor data froma pest detector (of the one or more pest detectors). At 106, the UI maydisplay the notification message and the sensor data. For example, inFIG. 6, the detector 100 may send the notification 624 and the data 626(e.g., including the sensor data 632) to one or more of the computingdevices 628. The app 634 may display the notification 624 and the data626 in the UI 702 of FIGS. 7 and 8.

FIG. 11 illustrates an example configuration of the computing device1100 that can be used to implement the systems and techniques describedherein, including the detector 100, the server 602, and one or more ofthe computing devices 628. The computing device 1100 may include one ormore processors 1102, a memory 1104, communication interfaces 1106, adisplay device 1108, other input/output (I/O) devices 1110, and one ormore mass storage devices 1112, configured to communicate with eachother, such as via system buses 1114 or other suitable connection. Thesystem buses 1114 may include multiple buses, such as memory devicebuses, storage device buses, power buses, video signal buses, and thelike. A single bus is illustrated in FIG. 11 purely for ease ofunderstanding.

The processors 1102 are one or more hardware devices that may include asingle processing unit or a number of processing units, all of which mayinclude single or multiple computing units or multiple cores. Theprocessors 1102 may be implemented as one or more microprocessors,microcomputers, microcontrollers, digital signal processors, centralprocessing units, graphics processing units, state machines, logiccircuitries, and/or any devices that manipulate signals based onoperational instructions. Among other capabilities, the processor 1102may be configured to fetch and execute computer-readable instructionsstored in the memory 1104, mass storage devices 1112, or othercomputer-readable media.

Memory 1104 and mass storage devices 1112 are examples of computerstorage media (e.g., memory storage devices) for storing instructionsthat can be executed by the processor 1102 to perform the variousfunctions described herein. For example, memory 1104 may include bothvolatile memory and non-volatile memory (e.g., RAM, ROM, or the like)devices. Further, mass storage devices 1112 may include hard diskdrives, solid-state drives, removable media, including external andremovable drives, memory cards, flash memory, floppy disks, opticaldisks (e.g., CD, DVD), a storage array, a network attached storage, astorage area network, or the like. Both memory 1104 and mass storagedevices 1112 may be collectively referred to as memory or computerstorage media herein, and may be a media capable of storingcomputer-readable, processor-executable program instructions as computerprogram code that can be executed by the processor 1102 as a particularmachine configured for carrying out the operations and functionsdescribed in the implementations herein.

The computing device 1100 may also include one or more communicationinterfaces 1106 for exchanging data via the network 106. Thecommunication interfaces 1106 can facilitate communications within awide variety of networks and protocol types, including wired networks(e.g., Ethernet, DOCSIS, DSL, Fiber, USB etc.) and wireless networks(e.g., WLAN, GSM, CDMA, 802.11, Bluetooth, Wireless USB, cellular,satellite, etc.), the Internet and the like. Communication interfaces1106 can also provide communication with external storage (not shown),such as in a storage array, network attached storage, storage areanetwork, or the like. A display device 1108, such as a monitor may beincluded in some implementations for displaying information and imagesto users. Other I/O devices 1110 may be devices that receive variousinputs from a user and provide various outputs to the user, and mayinclude a keyboard, a remote controller, a mouse, a printer, audioinput/output devices, and so forth.

The computer storage media, such as memory 1104 and mass storage devices1112, may be used to store software and data. For example, when thecomputing device 1100 is used to implement the detector 100, the memory1104 may be used to store the machine learning 612, the stored data 614,and the software 616. When the computing device 1100 is used toimplement the server 602, the memory 1104 may be used to store theremote machine learning 618, the new detector ML 622, and the database620. When the computing device 1100 is used to implement one of thecomputing devices 628, the memory 1104 may be used to store the app 634that is used to display the UI 702. In all three of the previousexamples, the memory 1104 may be used to store additional software 1116and additional data 1118. The additional software 1116 may includevision processing units (VPUs) to analyze image data captured by sensorsand neural network processing. For example, neural networks may be usedto analyze digital images and learn to identify images that includepests by analyzing example images that have been manually labeled as“<pest>” or “not a <pest>” and using the results to identify pests inother images. The neural networks automatically generate identifyingcharacteristics from processing the learning material.

The example systems and computing devices described herein are merelyexamples suitable for some implementations and are not intended tosuggest any limitation as to the scope of use or functionality of theenvironments, architectures and frameworks that can implement theprocesses, components and features described herein. Thus,implementations herein are operational with numerous environments orarchitectures, and may be implemented in general purpose andspecial-purpose computing systems, or other devices having processingcapability. Generally, any of the functions described with reference tothe figures can be implemented using software, hardware (e.g., fixedlogic circuitry) or a combination of these implementations. The term“module,” “mechanism” or “component” as used herein generally representssoftware, hardware, or a combination of software and hardware that canbe configured to implement prescribed functions. For instance, in thecase of a software implementation, the term “module,” “mechanism” or“component” can represent program code (and/or declarative-typeinstructions) that performs specified tasks or operations when executedon a processing device or devices (e.g., CPUs or processors). Theprogram code can be stored in one or more computer-readable memorydevices or other computer storage devices. Thus, the processes,components and modules described herein may be implemented by a computerprogram product.

Furthermore, this disclosure provides various example implementations,as described and as illustrated in the drawings. However, thisdisclosure is not limited to the implementations described andillustrated herein, but can extend to other implementations, as would beknown or as would become known to those skilled in the art. Reference inthe specification to “one implementation,” “this implementation,” “theseimplementations” or “some implementations” means that a particularfeature, structure, or characteristic described is included in at leastone implementation, and the appearances of these phrases in variousplaces in the specification are not necessarily all referring to thesame implementation.

Although the present invention has been described in connection withseveral embodiments, the invention is not intended to be limited to thespecific forms set forth herein. On the contrary, it is intended tocover such alternatives, modifications, and equivalents as can bereasonably included within the scope of the invention as defined by theappended claims.

What is claimed is:
 1. A detector comprising: one or more externalindicators; one or more processors; and one or more non-transitorycomputer readable media to store instructions executable by the one ormore processors to perform operations comprising: receiving sensor datafrom a sensor of the detector; determining, by a machine learningalgorithm, that the sensor data indicates a presence of a first type ofpest; sending a notification message to a computing device, thenotification message including at least a portion of the sensor data;receiving, from a server, an update to the machine learning algorithm;installing the update to create an updated machine learning algorithm;receiving second sensor data from the sensor; determining, by theupdated machine learning algorithm, that the second sensor dataindicates the presence of a second type of pest, wherein the second typeof pest is not recognized by the machine learning algorithm; based atleast in part on detecting, by a motion sensor, movement associated withthe first type of pest, providing power to a plurality of electrodes;electrocuting the first type of pest using the plurality of electrodes;and based at least in part on detecting, by the motion sensor, nomovement, stopping providing power to the plurality of electrodes. 2.The detector of claim 1, wherein determining, by the machine learningalgorithm, that the sensor data indicates the presence of the first typeof pest comprises: detecting, by the motion sensor, movement associatedwith the first type of pest; capturing, by an imaging sensor of thedetector, one or more digital images of the first type of pest; anddetermining, by the machine learning algorithm, that the one or moredigital images include the first type of pest.
 3. The detector of claim1, wherein: the computing device comprises one of a smartphone, atablet, or a virtual assistant enabled device.
 4. The detector of claim1, wherein: the notification message causes a pest detected indicator tobe displayed on a software application executing on the computingdevice.
 5. The detector of claim 1, the operations further comprising:detecting, using a wireless networking protocol, a presence of one ormore additional detectors; and creating a mesh network with the one ormore additional detectors, wherein the detector uses the mesh network tocommunicate with at least one of a computing device or a server.
 6. Thedetector of claim 1, further comprising: a bait receptacle; bait placedin the bait receptacle to lure the first type of pest; and twoelectrodes to kill the first type of pest using electrocution.
 7. Thedetector of claim 1, wherein the one or more external indicatorscomprise at least one of: a power indicator to indicate whether or notthe detector is powered on; a mode indicator to indicate whether or notthe detector is in a pest detection mode; a network connection statusindicator to indicate whether or not the detector is connected to awireless network; and a pest detected status indicator to indicatewhether or not the detector has detected a pest.
 8. A pest detectorcomprising: a plurality of sensors; a processor; and one or morenon-transitory computer readable storage media to store instructionsexecutable by the processor to perform operations comprising: receivingsensor data from a first sensor of the plurality of sensors;determining, by a machine learning algorithm, that the sensor dataindicates a presence of a first type of pest; sending a notificationmessage to a computing device, the notification message including atleast a portion of the sensor data; receiving, from a server, an updateto the machine learning algorithm from the server; installing the updateto create an updated machine learning algorithm; receiving second sensordata from the sensor; determining, by the updated machine learningalgorithm, that the second sensor data indicates the presence of asecond type of pest that is not recognized by the machine learningalgorithm; based at least in part on detecting, by a motion sensor,movement associated with the first type of pest, providing power to aplurality of electrodes; electrocuting the first type of pest using theplurality of electrodes; and based at least in part on detecting, by themotion sensor, no movement, stopping providing power to the plurality ofelectrodes.
 9. The pest detector of claim 8, wherein the plurality ofsensors includes at least one of: an imaging sensor, a microphone, astructured light sensor, an ultrasound sensor, a temperature sensor, anultrasonic sensor, a capacitive sensor, and a micropower impulse radarsensor.
 10. The pest detector of claim 8, wherein: the first sensor ispositioned to create a first pest detection zone that includes a firstportion of a wall including where the wall meets a ceiling; and a secondsensor of the plurality of sensors is positioned to create a second pestdetection zone that includes a remaining portion of the wall comprisingwhere the wall meets a floor.
 11. The pest detector of claim 8, wherein:the first sensor is positioned facing away from a wall to detectmovement of the first type of pest outside the wall; and a second sensorof the plurality of sensors is positioned to face the wall to detectmovement of the first type of pest within the wall.
 12. The pestdetector of claim 8, wherein: the updated machine learning algorithm istrained to detect pests in a geographic area in which the pest detectoris located.
 13. The pest detector of claim 8, the operations furthercomprising: detecting, by the motion sensor, movement associated withthe first type of pest; capturing, by an imaging sensor of the pestdetector, one or more digital images of the first type of pest; anddetermining, by the machine learning algorithm, that the one or moredigital images include the first type of pest.
 14. A pest detectorcomprising: one or more sensors; a processor; and one or morenon-transitory computer readable storage media to store instructionsexecutable by the processor to perform operations comprising: receivingsensor data from a sensor of the one or more sensors; determining, by amachine learning algorithm, that the sensor data indicates a presence ofa first type of pest; sending a notification message to a computingdevice, the notification message including at least a portion of thesensor data; receiving, from a server, an update to the machine learningalgorithm from the server; installing the update to create an updatedmachine learning algorithm; receiving second sensor data from thesensor; determining, by the updated machine learning algorithm, that thesecond sensor data indicates the presence of a second type of pest thatis not recognized by the machine learning algorithm; based at least inpart on detecting, by a motion sensor, movement associated with thefirst type of pest, providing power to a plurality of electrodes;electrocuting the first type of pest using the plurality of electrodes;and based at least in part on detecting, by the motion sensor, nomovement, stopping providing power to the plurality of electrodes. 15.The pest detector of claim 14, wherein: the notification message causesa pest detected indicator to be provided by a software applicationexecuting on the computing device.
 16. The pest detector of claim 14,further comprising one or more indicators including at least one of: apower indicator to indicate that the pest detector is either powered onor powered off; a mode indicator to indicate that the pest detector iseither in a low power mode or in a pest detection mode; and a networkconnection status indicator to indicate that the pest detector is eitherconnected to a wireless network or disconnected from the wirelessnetwork; and a pest detected indicator to indicate that a pest has beendetected or to indicate that no pest has been detected.
 17. The pestdetector of claim 14, further comprising: a bait receptacle; and anattractant placed in the bait receptacle to lure the first type of pest.18. The pest detector of claim 14, wherein the one or more sensorscomprise at least one of: an imaging sensor, a microphone, a structuredlight sensor, an ultrasound sensor, a temperature sensor, an ultrasonicsensor, a capacitive sensor, or a micropower impulse radar sensor. 19.The pest detector of claim 14, wherein the computing device comprisesone of a smartphone, a tablet, or a virtual assistant enabled device.20. The pest detector of claim 14, the operations further comprising:receiving ambient light data from an ambient light sensor of the pestdetector; determining that the ambient light data does not satisfies apredetermined threshold; and transitioning the pest detector from anactive mode to a low-power mode.